Overview

Dataset statistics

Number of variables8
Number of observations929
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory117.2 KiB
Average record size in memory129.1 B

Variable types

Categorical1
Numeric7

Alerts

filename has a high cardinality: 126 distinct valuesHigh cardinality
xmin is highly overall correlated with xmaxHigh correlation
ymin is highly overall correlated with ymaxHigh correlation
xmax is highly overall correlated with xminHigh correlation
ymax is highly overall correlated with yminHigh correlation
class has 254 (27.3%) zerosZeros
xmin has 51 (5.5%) zerosZeros
ymin has 19 (2.0%) zerosZeros

Reproduction

Analysis started2023-10-18 08:18:41.358220
Analysis finished2023-10-18 08:18:46.147514
Duration4.79 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

filename
Categorical

Distinct126
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Memory size66.4 KiB
000000000196.jpg
 
42
000000000164.jpg
 
40
000000000315.jpg
 
38
000000000257.jpg
 
33
000000000110.jpg
 
24
Other values (121)
752 

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters14864
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)1.3%

Sample

1st row000000000260.jpg
2nd row000000000260.jpg
3rd row000000000260.jpg
4th row000000000260.jpg
5th row000000000260.jpg

Common Values

ValueCountFrequency (%)
000000000196.jpg 42
 
4.5%
000000000164.jpg 40
 
4.3%
000000000315.jpg 38
 
4.1%
000000000257.jpg 33
 
3.6%
000000000110.jpg 24
 
2.6%
000000000149.jpg 22
 
2.4%
000000000643.jpg 20
 
2.2%
000000000294.jpg 20
 
2.2%
000000000540.jpg 20
 
2.2%
000000000113.jpg 18
 
1.9%
Other values (116) 652
70.2%

Length

2023-10-18T16:18:46.185802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
000000000196.jpg 42
 
4.5%
000000000164.jpg 40
 
4.3%
000000000315.jpg 38
 
4.1%
000000000257.jpg 33
 
3.6%
000000000110.jpg 24
 
2.6%
000000000149.jpg 22
 
2.4%
000000000643.jpg 20
 
2.2%
000000000294.jpg 20
 
2.2%
000000000540.jpg 20
 
2.2%
000000000113.jpg 18
 
1.9%
Other values (116) 652
70.2%

Most occurring characters

ValueCountFrequency (%)
0 8601
57.9%
. 929
 
6.2%
j 929
 
6.2%
p 929
 
6.2%
g 929
 
6.2%
4 424
 
2.9%
1 413
 
2.8%
3 348
 
2.3%
5 309
 
2.1%
6 267
 
1.8%
Other values (4) 786
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11148
75.0%
Lowercase Letter 2787
 
18.8%
Other Punctuation 929
 
6.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8601
77.2%
4 424
 
3.8%
1 413
 
3.7%
3 348
 
3.1%
5 309
 
2.8%
6 267
 
2.4%
2 245
 
2.2%
9 224
 
2.0%
8 166
 
1.5%
7 151
 
1.4%
Lowercase Letter
ValueCountFrequency (%)
j 929
33.3%
p 929
33.3%
g 929
33.3%
Other Punctuation
ValueCountFrequency (%)
. 929
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12077
81.2%
Latin 2787
 
18.8%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8601
71.2%
. 929
 
7.7%
4 424
 
3.5%
1 413
 
3.4%
3 348
 
2.9%
5 309
 
2.6%
6 267
 
2.2%
2 245
 
2.0%
9 224
 
1.9%
8 166
 
1.4%
Latin
ValueCountFrequency (%)
j 929
33.3%
p 929
33.3%
g 929
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14864
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8601
57.9%
. 929
 
6.2%
j 929
 
6.2%
p 929
 
6.2%
g 929
 
6.2%
4 424
 
2.9%
1 413
 
2.8%
3 348
 
2.3%
5 309
 
2.1%
6 267
 
1.8%
Other values (4) 786
 
5.3%

width
Real number (ℝ)

Distinct27
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean595.25404
Minimum333
Maximum640
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2023-10-18T16:18:46.261704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum333
5-th percentile426.4
Q1591
median640
Q3640
95-th percentile640
Maximum640
Range307
Interquartile range (IQR)49

Descriptive statistics

Standard deviation79.209865
Coefficient of variation (CV)0.13306901
Kurtosis0.44494725
Mean595.25404
Median Absolute Deviation (MAD)0
Skewness-1.412371
Sum552991
Variance6274.2026
MonotonicityNot monotonic
2023-10-18T16:18:46.345041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
640 680
73.2%
480 56
 
6.0%
500 52
 
5.6%
427 19
 
2.0%
416 18
 
1.9%
520 16
 
1.7%
600 12
 
1.3%
446 11
 
1.2%
448 11
 
1.2%
381 9
 
1.0%
Other values (17) 45
 
4.8%
ValueCountFrequency (%)
333 2
 
0.2%
359 2
 
0.2%
360 1
 
0.1%
375 4
 
0.4%
381 9
1.0%
409 2
 
0.2%
416 18
1.9%
423 4
 
0.4%
426 5
 
0.5%
427 19
2.0%
ValueCountFrequency (%)
640 680
73.2%
638 2
 
0.2%
612 1
 
0.1%
600 12
 
1.3%
591 2
 
0.2%
565 2
 
0.2%
520 16
 
1.7%
516 2
 
0.2%
512 3
 
0.3%
500 52
 
5.6%

height
Real number (ℝ)

Distinct38
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean477.56943
Minimum218
Maximum640
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2023-10-18T16:18:46.439097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum218
5-th percentile333
Q1427
median480
Q3484
95-th percentile640
Maximum640
Range422
Interquartile range (IQR)57

Descriptive statistics

Standard deviation91.966002
Coefficient of variation (CV)0.19257096
Kurtosis0.5486314
Mean477.56943
Median Absolute Deviation (MAD)53
Skewness0.10094682
Sum443662
Variance8457.7454
MonotonicityNot monotonic
2023-10-18T16:18:46.525432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
480 309
33.3%
640 150
16.1%
427 100
 
10.8%
426 53
 
5.7%
428 44
 
4.7%
375 31
 
3.3%
500 26
 
2.8%
425 22
 
2.4%
481 19
 
2.0%
484 17
 
1.8%
Other values (28) 158
17.0%
ValueCountFrequency (%)
218 17
1.8%
226 1
 
0.1%
248 7
0.8%
312 5
 
0.5%
313 4
 
0.4%
327 6
 
0.6%
332 4
 
0.4%
333 7
0.8%
336 11
1.2%
360 3
 
0.3%
ValueCountFrequency (%)
640 150
16.1%
612 1
 
0.1%
611 2
 
0.2%
600 4
 
0.4%
580 13
 
1.4%
573 7
 
0.8%
536 4
 
0.4%
513 1
 
0.1%
500 26
 
2.8%
495 2
 
0.2%

class
Real number (ℝ)

Distinct71
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.335845
Minimum0
Maximum79
Zeros254
Zeros (%)27.3%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2023-10-18T16:18:46.619974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median26
Q350
95-th percentile73
Maximum79
Range79
Interquartile range (IQR)50

Descriptive statistics

Standard deviation25.730559
Coefficient of variation (CV)0.90805687
Kurtosis-1.2656234
Mean28.335845
Median Absolute Deviation (MAD)25
Skewness0.319161
Sum26324
Variance662.06166
MonotonicityNot monotonic
2023-10-18T16:18:46.718448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 254
27.3%
2 46
 
5.0%
41 36
 
3.9%
56 35
 
3.8%
73 29
 
3.1%
45 28
 
3.0%
51 24
 
2.6%
44 22
 
2.4%
77 21
 
2.3%
26 19
 
2.0%
Other values (61) 415
44.7%
ValueCountFrequency (%)
0 254
27.3%
1 6
 
0.6%
2 46
 
5.0%
3 5
 
0.5%
4 6
 
0.6%
5 7
 
0.8%
6 3
 
0.3%
7 12
 
1.3%
8 6
 
0.6%
9 14
 
1.5%
ValueCountFrequency (%)
79 5
 
0.5%
77 21
2.3%
76 1
 
0.1%
75 2
 
0.2%
74 9
 
1.0%
73 29
3.1%
72 5
 
0.5%
71 6
 
0.6%
69 5
 
0.5%
68 3
 
0.3%

xmin
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct454
Distinct (%)48.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean251.42734
Minimum0
Maximum628
Zeros51
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2023-10-18T16:18:46.822170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1108
median236
Q3387
95-th percentile546
Maximum628
Range628
Interquartile range (IQR)279

Descriptive statistics

Standard deviation172.53535
Coefficient of variation (CV)0.68622351
Kurtosis-0.98640253
Mean251.42734
Median Absolute Deviation (MAD)140
Skewness0.25898717
Sum233576
Variance29768.448
MonotonicityNot monotonic
2023-10-18T16:18:46.916662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 51
 
5.5%
1 13
 
1.4%
3 7
 
0.8%
88 6
 
0.6%
2 6
 
0.6%
434 5
 
0.5%
153 5
 
0.5%
218 5
 
0.5%
249 5
 
0.5%
332 5
 
0.5%
Other values (444) 821
88.4%
ValueCountFrequency (%)
0 51
5.5%
1 13
 
1.4%
2 6
 
0.6%
3 7
 
0.8%
4 3
 
0.3%
5 1
 
0.1%
6 2
 
0.2%
7 1
 
0.1%
9 1
 
0.1%
11 2
 
0.2%
ValueCountFrequency (%)
628 1
 
0.1%
623 3
0.3%
619 1
 
0.1%
617 1
 
0.1%
615 1
 
0.1%
611 1
 
0.1%
608 1
 
0.1%
606 1
 
0.1%
604 1
 
0.1%
601 1
 
0.1%

ymin
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct370
Distinct (%)39.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean198.16577
Minimum0
Maximum596
Zeros19
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2023-10-18T16:18:47.014583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q1111
median197
Q3274
95-th percentile387
Maximum596
Range596
Interquartile range (IQR)163

Descriptive statistics

Standard deviation115.98576
Coefficient of variation (CV)0.58529665
Kurtosis-0.15188034
Mean198.16577
Median Absolute Deviation (MAD)79
Skewness0.29114524
Sum184096
Variance13452.697
MonotonicityNot monotonic
2023-10-18T16:18:47.105888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19
 
2.0%
194 10
 
1.1%
1 9
 
1.0%
2 8
 
0.9%
192 8
 
0.9%
197 7
 
0.8%
203 7
 
0.8%
325 6
 
0.6%
138 6
 
0.6%
269 6
 
0.6%
Other values (360) 843
90.7%
ValueCountFrequency (%)
0 19
2.0%
1 9
1.0%
2 8
0.9%
3 5
 
0.5%
4 4
 
0.4%
5 1
 
0.1%
6 2
 
0.2%
8 2
 
0.2%
9 1
 
0.1%
11 1
 
0.1%
ValueCountFrequency (%)
596 1
0.1%
595 1
0.1%
577 1
0.1%
553 1
0.1%
544 1
0.1%
542 1
0.1%
536 1
0.1%
530 1
0.1%
522 1
0.1%
509 1
0.1%

xmax
Real number (ℝ)

Distinct477
Distinct (%)51.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean353.39505
Minimum6
Maximum640
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2023-10-18T16:18:47.204962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile80.8
Q1215
median361
Q3484
95-th percentile625
Maximum640
Range634
Interquartile range (IQR)269

Descriptive statistics

Standard deviation169.40418
Coefficient of variation (CV)0.47936206
Kurtosis-1.049471
Mean353.39505
Median Absolute Deviation (MAD)136
Skewness-0.035155284
Sum328304
Variance28697.776
MonotonicityNot monotonic
2023-10-18T16:18:47.296886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
640 24
 
2.6%
639 11
 
1.2%
480 8
 
0.9%
403 7
 
0.8%
562 7
 
0.8%
294 5
 
0.5%
409 5
 
0.5%
229 5
 
0.5%
379 5
 
0.5%
425 5
 
0.5%
Other values (467) 847
91.2%
ValueCountFrequency (%)
6 1
0.1%
18 1
0.1%
23 1
0.1%
24 2
0.2%
28 1
0.1%
33 2
0.2%
34 1
0.1%
35 1
0.1%
36 1
0.1%
37 1
0.1%
ValueCountFrequency (%)
640 24
2.6%
639 11
1.2%
638 1
 
0.1%
637 1
 
0.1%
635 1
 
0.1%
634 1
 
0.1%
631 2
 
0.2%
630 2
 
0.2%
628 1
 
0.1%
627 1
 
0.1%

ymax
Real number (ℝ)

Distinct401
Distinct (%)43.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean307.89559
Minimum18
Maximum640
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2023-10-18T16:18:47.460702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile96.4
Q1214
median303
Q3392
95-th percentile544.2
Maximum640
Range622
Interquartile range (IQR)178

Descriptive statistics

Standard deviation131.95127
Coefficient of variation (CV)0.42855851
Kurtosis-0.19694987
Mean307.89559
Median Absolute Deviation (MAD)89
Skewness0.28964623
Sum286035
Variance17411.139
MonotonicityNot monotonic
2023-10-18T16:18:47.564160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
480 14
 
1.5%
473 10
 
1.1%
479 9
 
1.0%
235 8
 
0.9%
368 8
 
0.9%
474 8
 
0.9%
640 8
 
0.9%
285 7
 
0.8%
370 6
 
0.6%
256 6
 
0.6%
Other values (391) 845
91.0%
ValueCountFrequency (%)
18 1
 
0.1%
29 2
0.2%
33 2
0.2%
41 1
 
0.1%
44 1
 
0.1%
46 1
 
0.1%
47 2
0.2%
49 1
 
0.1%
52 2
0.2%
53 3
0.3%
ValueCountFrequency (%)
640 8
0.9%
639 3
 
0.3%
637 1
 
0.1%
635 1
 
0.1%
632 4
0.4%
630 2
 
0.2%
628 3
 
0.3%
625 1
 
0.1%
624 1
 
0.1%
614 1
 
0.1%

Interactions

2023-10-18T16:18:45.295698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:41.723027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:42.318958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:42.933604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:43.528935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:44.120628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:44.697747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:45.385214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:41.815555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:42.400015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:43.021370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:43.612415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:44.203066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:44.783047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:45.467065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:41.895140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:42.473709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:43.100086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:43.687785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:44.282053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:44.863309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:45.560789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:41.986051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:42.558542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:43.188912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:43.776230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:44.369768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:44.954407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:45.643032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:42.070643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:42.689271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:43.273885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:43.859370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:44.451852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:45.045750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:45.795368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:42.153561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:42.768072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:43.359483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:43.941829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:44.533112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:45.128384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:45.876190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:42.236599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:42.848428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:43.444589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:44.027044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:44.615048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T16:18:45.212147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-10-18T16:18:47.644636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
widthheightclassxminyminxmaxymax
width1.000-0.410-0.1270.2870.0140.265-0.146
height-0.4101.0000.103-0.2100.112-0.0950.326
class-0.1270.1031.000-0.0410.0420.035-0.011
xmin0.287-0.210-0.0411.0000.1160.699-0.244
ymin0.0140.1120.0420.1161.000-0.1130.505
xmax0.265-0.0950.0350.699-0.1131.0000.100
ymax-0.1460.326-0.011-0.2440.5050.1001.000

Missing values

2023-10-18T16:18:45.987492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-18T16:18:46.098529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

filenamewidthheightclassxminyminxmaxymax
0000000000260.jpg500333529368251
1000000000260.jpg5003330261139312309
2000000000260.jpg50033301714240170
3000000000260.jpg5003332238175271190
4000000000260.jpg50033328332232375293
5000000000260.jpg50033328373222402292
6000000000260.jpg50033328334205395245
7000000000089.jpg64048043502105528237
8000000000089.jpg64048043476127496233
9000000000089.jpg6404804352899555237
filenamewidthheightclassxminyminxmaxymax
919000000000127.jpg64048113014864230
920000000000127.jpg6404811315786201102
921000000000127.jpg640481252940398104
922000000000127.jpg64048125131316458
923000000000127.jpg64048126111123581286
924000000000127.jpg6404810444846644
925000000000127.jpg6404811310699171148
926000000000127.jpg64048113196147249183
927000000000127.jpg6404816026595479118
928000000000127.jpg64048125104113246